We use a ResNet-101 [1]}, pre-trained on ImageNet [2]} to extract the high-dimensional features of the entire image \(f_i\) .
The features \(f_o\) , encoding the detected objects in each image using the VGG-16 [3]} model, pre-trained on ImageNet [2]} for the object detection task. VGG-16 extracts the feature vectors of objects using information on the Regions of Interest (ROIs) in the image, detected using the YOLOv3 object detection model [5]}.
We set a confidence threshold for ROI selection to 0.8 and consider a maximum of 12 objects per image, as in Yang et al. [6]}.
Each ROI is labelled with 80 object categories from the COCO [7]} dataset with the addition of the background category to account for images with no apparent objects.
Both \(f_o\)  and \(f_i\)  are \(d\) -dimensional features, where \(d=4096\) . Therefore, \(C\in \mathbb {R}^{d\times (2+K)}\)  in GIP [6]}.
